一种新的蚁群算法及其在飞行器设计中的应用  

New ant colony algorithm and its application on optimization design of flight vehicle

作　　者：车竞[1] 唐硕[2] 王文正[1] 何开锋[1] 

机构地区：[1]中国空气动力研究与发展中心,绵阳621000 [2]西北工业大学航天学院,西安710072

出　　处：《航空动力学报》2009年第2期262-268,共7页Journal of Aerospace Power

基　　金：国家自然科学基金(10572115);中国博士后科学基金(20080431373)

摘　　要：尝试将蚁群算法引入飞行器优化设计领域,为此建立了适用于高维、多目标、多约束优化问题的连续空间蚁群算法,并以高超声速飞行器气动布局的多目标优化设计为例进行了验证.优化设计结果与采用遗传算法得到的优化结果进行了对比,指出了蚁群算法的优点.该研究可为蚁群算法应用于复杂、高维的大规模飞行器设计问题提供参考.Ant colony algorithm （ACA） is a new bionic optimization algorithm developed in recent years. With global and efficient characteristics, it has been applied in discontinuous space successfully. To introduce it to aircraft design field, a high dimensional, multi-objective and multi-restrained ACA for continuous space was built. In an example, it was applied to the multi-objective optimization design of aerodynamic configuration for hypersonic cruise vehicle （HCV）. Through comparison with Pareto genetic algorithms （GA）, ACA shows its advantage. Finally, from our research work, ACA has great reference values for complex, multidimensional and large-scale optimization problems in aircraft design field.

关 键 词：多目标蚁群算法 连续空间 高超声速飞行器 气动布局 优化设计 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构] V221.3[自动化与计算机技术—计算机科学与技术]